Do evaluation

JenkinsfileEvaluation

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+pipeline {
+    agent {
+        dockerfile true
+    }
+
+    parameters{
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'BUILD_SELECTOR'
+        )
+    }
+
+    triggers {
+        upstream(upstreamProjects: 's495719-training', threshold: hudson.model.Result.SUCCESS)
+    }
+
+    stages {
+        stage('Git') {
+            steps {
+                git(
+                    url: "https://git.wmi.amu.edu.pl/s495719/ium_495719.git",
+                    branch: "main"
+                )
+            }
+        }
+        stage('CopyArtifacts') {
+            steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's495719-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                copyArtifacts fingerprintArtifacts: true, projectName: 's495719-training', selector: buildParameter('BUILD_SELECTOR')
+                copyArtifacts fingerprintArtifacts: true, projectName: 's495719-evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
+            }
+        }
+        stage('Script') {
+            steps {
+                sh 'chmod 777 ./evaluate.py'
+                sh "python3 ./evaluate.py ${currentBuild.number}"
+            }
+        }
+        stage('CreateArtifacts') {
+            steps {
+                archiveArtifacts artifacts: 'hp_test_predictions.csv,hp_test_metrics.csv'
+            }
+        }
+    }
+}

evaluate.py

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+import pandas as pd
+import numpy as np
+import sys
+import os
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+from keras.models import load_model
+from helper import prepare_tensors
+
+build_number = int(sys.argv[1])
+
+hp_test = pd.read_csv('hp_test.csv')
+X_test, Y_test = prepare_tensors(hp_test)
+
+model = load_model('hp_model.h5')
+
+test_predictions = model.predict(X_test)
+
+predictions_df = pd.DataFrame(test_predictions, columns=["Predicted_Price"])
+predictions_df.to_csv('hp_test_predictions.csv', index=False)
+
+rmse = np.sqrt(mean_squared_error(Y_test, test_predictions))
+mae = mean_absolute_error(Y_test, test_predictions)
+r2 = r2_score(Y_test, test_predictions)
+
+metrics_df = pd.DataFrame({
+    'Build_Number': [build_number],
+    'RMSE': [rmse],
+    'MAE': [mae],
+    'R2': [r2]
+})
+
+metrics_file = 'hp_test_metrics.csv'
+if os.path.isfile(metrics_file):
+    existing_metrics_df = pd.read_csv(metrics_file)
+    updated_metrics_df = pd.concat([existing_metrics_df, metrics_df], ignore_index=True)
+else:
+    updated_metrics_df = metrics_df
+
+updated_metrics_df.to_csv(metrics_file, index=False)